Research On Adaptive Digital Beam Forming Technique Based On Array Signal Processing

Array signal processing is a key component of the signal processing. On the modern battlefield, radar systems are facing a very complex application environment; therefore, there are more demands on the effect of radar systems. Radar signal processing is required to be real-time, anti-jamming, and anti-radiation. Since array signal processing has many excellent features, it is widely used in the radar field.Adaptive digital beam forming technology can well utilize the information that the array antenna receives, by using adaptive weighting operation with certain constraints, to form the beam in a specific orientation, and simultaneously to achieve interference suppression, noise removal and beam scanning. ADBF algorithm is crucial for using ADBF technology. This paper focuses on adaptive digital beam forming technology, which is one of the two core parts of array signal processing. The aim of this research is to optimize the algorithm from three perspectives: reducing the errors, increasing the robustness, and improving the converging speed. ADBF technology can be better used in the system applications. The main work of this paper:1. Based on the analyses for the different temporal and spatial expressions of the array signal processing model, we introduced three statistical models for different array antenna application scenarios and also illustrated the model performance and their areas of application. Then we mainly focused on the analyses for array covariance matrix and source number estimation.2. According to the expected effect of beam forming technology, we selected the digital way to achieve beam forming, and discussed the optimal weight vector of the digital beam forming(DBF) in detail. ADBF technology combines adaptive signal processing and spatial filtering processing. Given the known conditions and the different filtering demands, it generates three adaptive beam forming optimization criteria. By simulating these three criteria, we found that although we achieved different weighting expressions for different conditions, the ideal weighting values are the same, and all of them are the optimal solutions. Therefore we can select the most appropriate weighting criteria for system applications based on environment changes.3. The principle of beam former, as a part of ADBF technology, was deeply discussed. By comparing the principles and performance of the traditional filtering and the adaptive filtering, we provided two adaptive filters referring to different optimization criteria- Wiener filter and Capon filter. Based on the adaptive method for weighted vector updates, detailed analyses were performed on the least mean square error(LMS) algorithm. Then we ran a simulation with the corresponding performance analysis for this algorithm and also compared it with the effect of recursive least squares(RLS) algorithm.